Title :
Block classification using t-statistics and L∞ distortion measure
Author :
Suthaharan, Shan
Author_Institution :
Gippsland Sch. of Comput. & Inf. Technol., Monash Univ., Clayton, Vic., Australia
Abstract :
This paper presents a new method for block classification, at the decoding stage, in digital image and video coding. Linear filters have been used to reduce the blocking artifacts caused by the block-based transforms used in the digital video processing. However, the linear filters have been applied to every block on the image regardless of their degree of visibility. In this paper, a block classification algorithm is proposed to identify the blocks that are apparent and significantly contribute to the overall blocking artifacts so that these blocks can be filtered out to reduce the blockiness
Keywords :
coding errors; decoding; digital filters; image classification; image coding; interference suppression; statistical analysis; transform coding; video coding; L∞ distortion measure; block classification; block-based transforms; blockiness; blocking artifacts; decoding; digital image coding; digital video processing; linear filters; t-statistics; video coding; visibility; Classification algorithms; Degradation; Distortion measurement; Filtering; Maximum likelihood detection; Nonlinear filters; Pixel; Random variables; Statistical analysis; Testing;
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
DOI :
10.1109/ICICS.1997.652122
